How to compress your Keras model x5 smaller with TensorFlow model optimization | DLology

prune_train.py

# Add a pruning step callback to peg the pruning step to the optimizer's
# step. Also add a callback to add pruning summaries to tensorboard
callbacks = [
    sparsity.UpdatePruningStep(),
    sparsity.PruningSummaries(log_dir=logdir, profile_batch=0)
]

new_pruned_model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          callbacks=callbacks,
          validation_data=(x_test, y_test))

score = new_pruned_model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])